Assimilation of Pseudo-GLM Observations Into a Storm Scale

Numerical Model Using the Ensemble Kalman Filter

Blake AllenUniversity of Oklahoma

Edward MansellNOAA / National Severe Storms Laboratory

Funding Provided by NOAA-NESDIS/JCSDA

Background: Pseudo-GLM ObsFlash observations were generated from LMA data, using a flash separation algorithm(MacGorman et al 2008) to specify individualflashes.

Each flash was then mapped to an approximately8km x 8km two dimensional grid.

For each flash that crossed a grid box, a flashevent was added to the box’s total.

Example Set Of pGLM Observations

-> Flash extent density ~ 80 min-1

-> Flash extent density ~ 1 min-1

Lat

Lon

May 8, 2003 22:09Z

EnKF SetupEnsemble Square Root filter used to produce analyses at 1, 3,or 5 minute intervals with a 40 member ensemble.

Pseudo-obs were placed at 6500 m height, with avertical localization radius of 35 km and a 16 kmhorizontal localization radius.

Observation error was set to 10%-15% of the maximumflash rate in each storm.

Thermal bubbles and smooth noise (Caya 2005) were used toinitiate convection and create/maintain ensemble spread.

Observation OperatorsFirst guesses at observation operators were produced by finding a linear fit between flash rate and microphysical variables from simulations using an explicit lightning model while assimilating radial velocity radar data.

Various linear relationships between graupel mass andflash rate, graupel volume and flash rate, andNon-inductive charging and flash rate were tested.

The best results were found with the relationshipFlash extent density = (0.017)*(graupel volume)

Observed During STEPS

Moved through an area well-sampled by the STEPS LMA

Radar data available from CHILL radar

Low-shear, low-CAPE environment

Evolved through multiple formations throughout its lifetime

Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories.

6 June 2000 Airmass Storm

6 June 2000 Results

Ensemble Mean Reflectivity22:30Z June 6 (1 min. assimilation)

Observed Reflectivity22:30Z June 6

6 June 2000 Airmass Storm

6 June 2000 Results

Ensemble Mean Reflectivity00:00Z June 7 (1 min. assimilation)

Observed Reflectivity00:00Z June 7

6 June 2000 Airmass Storm

6 June 2000 Results

Ensemble Mean Reflectivity01:00Z June 7 (1 min. assimilation)

Observed Reflectivity01:00Z June 7

6 June 2000 Airmass Storm

6 June 2000 Results

5 min pGLM assimilation

1 min pGLM assimilation

Vr assim5 min pGLM assimilation

1 min pGLM assimilation

Vr assimilation

Min = 0 m/s

Max = 35 m/s

Min = 0 km3

Max = 700 km3

Maximum Updraft Speed

6 June 2000 Airmass Storm

Graupel Volume > 0.5 g/kg

Moved through an area well-sampled by the Oklahoma LMA

Radar data available from KTLX WSR-88D, and previous work has been done using the storm as a test case for assimilation of radar velocity and reflectivity data (Dowell et al 2010).

High-shear, high-CAPE environment

Produced multiple tornadoes, including a long-tracked F4 that struck Moore OK and other parts of the Oklahoma City metro area.

Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories.

8 May 2003 Supercell

22:09Z

8 May 2003 Supercell

Ens. Mean reflectivity

Near-surface radar reflectivity around the time of the first tornado

Observed Reflectivity22:09Z22:09Z

Single member reflectivity

8 May 2003 Supercell

Vr assimilation1min pGLM assim

5 min pGLM assimilation

Vr assimilation

1min pGLM assimilation

5 min pGLM assimilation

Min = 0 km3

Max = 12000 km3

Min = 0 m/s

Max = 70 m/s Maximum Updraft Speed

Graupel Volume > 0.5 g/kg

8 May 2003 Results8 May 2003 SupercellProbabiliy of Vorticity > 0.016 s-1 at 1.75 km model height

Vr assimilation 1 minute pGLM assimilation

8 May 2003 Supercell

Conclusions1.) EnKF assimilation of pseudo-GLM data, using a single observation operator,

can produce analyses that capture the basic reflectivity structure of multiple storm types, and can produce maximum updraft speeds comparable to those obtained when assimilating Doppler radar radial velocity.

2.) In the supercell case, using high temporal resolution data captured the development of the low-level mesocyclone.

3.) At lower temporal resolutions, the strength of both storms dropped later in the runs, although this may be case specific – 8 May had a capping inversion and 6 June had weak instability.

4.) Since the GLM will produce observations over a much larger area than radar

observations are available, these results show promise that EnKF assimilation of GLM data can be useful stand-in for assimilation of radial velocity data in areas where radar data is lacking or of poor quality.

Summary / Conclusions

ReferencesA. Caya et al 2005: A comparison between the 4DVAR and the ensemble Kalman filter

techniques for radar data assimilation. Monthly Weather ReviewD. Dowell et al 2010: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May

2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses. Monthly Weather Review

D. MacGorman et al 2008: The Thunderstorm Electrification and Lightning Experiment. BAMS

AcknowledgementsThank you to Kristin Calhoun for converting LMA data to pseudo-GLM observations, and to David Dowell for providing radar data for the 6 June case.Funding Provided by NOAA-NESDIS/JCSDA

Questions?

EnKF Assimilation of Pseudo-GLM Observations (8 May 2003 Moore, OK Supercell)

Run A: Flash Rate = 0.017( Graupel Volume ) Run B: Flash Rate = 0.039( NI charge sep. rate)

Simulated Reflectivity Simulated Reflectivity

Probability of Vorticity > 0.016 s-1 Probability of Vorticity > 0.016 s-1

Observation Operator Comparison